Ai governance

AI Governance

Aliases: AI usage policy, EU AI Act, AI risk classification, model governance, responsible AI, AI compliance, acceptable use policy for AI, AI risk assessment

Maturity: Production

Description: Classify AI use cases by regulatory risk tier, enforce organizational AI acceptable use policies, determine transparency and human oversight requirements, track data lineage for AI-assisted outputs, and generate compliance artifacts for model governance.

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Overview

AI is moving from experimental to mission-critical, but governance hasn't kept pace. This skill embeds AI risk assessment and compliance into your operational workflows — not as a retrospective audit, but as a real-time guard rail.

The skill maps every proposed AI use case against:

1. EU AI Act risk tiers (Unacceptable → High → Limited → Minimal)
2. Your organization's AI acceptable use policy (from config/org-config.yaml)
3. Transparency & disclosure requirements for end users/customers
4. Human oversight obligations — defining what "meaningful review" actually means
5. Data lineage tracking — so downstream consumers know what's AI-generated vs. human-authored

This is not optional compliance theater. If your use case triggers a High or Unacceptable risk classification, the skill surfaces mandatory obligations that must be satisfied before deployment.

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Core Capabilities

1. AI System Risk Classification

Input: Description of a proposed AI use case (e.g., "Using an LLM to generate hiring recommendations based on CV analysis")

Process:

• Extract key attributes: data involved, decision autonomy, affected population, reversibility, sector
• Cross-reference against EU AI Act Annex III (high-risk) and Annex II (limited-risk) definitions
• Map to internal risk tier framework (Minimal → Limited → High → Unacceptable)
• Return: risk tier, justification, applicable obligations

Output Example:

Risk Tier: HIGH
Rationale: AI system used to support hiring decisions. EU AI Act Article 6(2) classifies 
employment decisions as high-risk. Impacts fundamental rights (employment opportunity).
Applicable Obligations:
  - High-quality training data (Article 10)
  - Documented risk assessment (Article 9)
  - Performance monitoring (Article 26)
  - Human oversight by qualified personnel (Article 26(3))
  - Transparent documentation (Article 13)

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2. AI Usage Policy Enforcement

Input: Proposed AI use case (description + risk tier classification result)

Process:

• Load org's AI acceptable use policy from config/org-config.yaml → ai-policies section
• Check against prohibited uses:
  • Automated hiring decisions without human review
  • Autonomous financial transactions above thresholds
  • Processing of special category data (health, biometric, criminal) without explicit safeguards
  • Real-time remote biometric identification (unless carve-out applies)
  • Subliminal/manipulative uses
  • Social scoring
  • Unauthorized profiling
• Return: policy compliance status, flagged violations, required mitigations

Configuration Reference:

ai-policies:
  prohibited-uses:
    - hiring-decisions-unreviewed
    - autonomous-financial-transfers
    - special-category-processing
    - real-time-biometric-id
  oversight-thresholds:
    high-risk-review: true
    approval-required-by: ["Chief Risk Officer", "Chief Compliance Officer"]
  disclosure-required: true
  data-retention-limits: "P1Y"  # 1 year post-decision

Output:

• PASS / BLOCKED / CONDITIONAL (requires mitigations)
• If BLOCKED: which policy was violated, no exceptions possible
• If CONDITIONAL: list mitigations needed to proceed (e.g., "Require 4-eyes review gate for all hiring recommendations")

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3. Transparency & Disclosure Requirements

Input: Risk tier + use case description + affected audience (employees, customers, public)

Process:

• Determine if users/customers must be informed they're interacting with AI (EU AI Act Article 52)
• Identify when explicit consent is required vs. just disclosure
• Generate templated disclosure language
• Check internal disclosure policy thresholds

Output: Generates disclosure language like:

TRANSPARENCY DISCLOSURE (Required)

[Your interaction with] this system uses artificial intelligence to [summarize: function]. 
The AI system processes [data categories] to generate [output type].

A human [job title] has reviewed [frequency] of outputs before [action taken].
To appeal or request human review, contact [support channel].

Data generated from this interaction will be retained for [duration] and used for [purposes].

Also produces:

• Timing of disclosure (before interaction, during, after)
• Consent requirement (explicit Y/N)
• Revocation mechanism (how user opts out)
• Mapping to EU AI Act Article 52 (transparency) + GDPR Article 21 (right to object)

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4. Human Oversight Requirements

For High and Unacceptable risk tiers, the skill doesn't just say "require human oversight" — it defines what that means operationally.

Input: Risk tier + use case + available data + existing guardrails

Output Example:

MANDATORY HUMAN OVERSIGHT SPECIFICATION

Risk Tier: HIGH
Use Case: AI-assisted hiring recommendation system

REVIEW TRIGGER: Every candidate recommendation for roles above Director level
REVIEWER QUALIFICATION: Hiring Manager + Talent Acquisition Lead (segregation required)
REVIEW SCOPE (Minimum):
  - Verify AI reasoning aligns with legal criteria (EEOC Article 106)
  - Check for disparate impact signals in recommendation patterns
  - Validate training data quality assumptions
  - Confirm no personal data from external sources was used without consent

DECISION AUTHORITY:
  - Reviewer may override AI recommendation without escalation
  - Reviewer must document override rationale in decision log
  - Overrides >10% of AI recommendations → trigger root cause analysis

DOCUMENTATION REQUIRED:
  - Reviewer name, date, time of review
  - Override decision (if applicable) + justification
  - Feedback to ML team for model retraining signal
  - Audit trail (immutable, tamper-evident log)

AUDIT & EFFECTIVENESS:
  - Monthly sample audit (10% of reviewed decisions)
  - Quarterly effectiveness metrics: false positive rate, override frequency
  - Report to Chief Risk Officer if override rate diverges >5% from baseline

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5. Data Lineage Tagging for AI

Input: Any content or data artifact (generated, augmented, or influenced by AI)

Process:

• Tag outputs with metadata:
  • AI_GENERATED: AI created this from scratch
  • AI_AUGMENTED: Human + AI collaborative output
  • AI_INFLUENCED: AI provided input; human made final decision
  • HUMAN_ONLY: No AI involvement
• Capture: which AI model, version, timestamp, training data cutoff
• Link to transparency obligations (downstream consumers must know)

Output: Metadata object for every artifact:

{
  "ai-lineage": {
    "status": "AI_AUGMENTED",
    "models": [
      {
        "name": "claude-opus-4",
        "version": "20250201",
        "timestamp": "2026-04-12T14:32:00Z",
        "training-data-cutoff": "2025-02-01"
      }
    ],
    "human-involvement": {
      "created-by": "[email protected]",
      "reviewed-by": "[email protected]",
      "review-date": "2026-04-12",
      "override": false
    },
    "disclosure-required": true,
    "retention-until": "2027-04-12"
  }
}

Downstream systems use this to:

• Trigger required disclosures automatically
• Enforce retention policies
• Generate audit trails
• Support subject access requests (GDPR Article 15)

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Integration with Sentinel Stack

This skill consumes signals from the core toolkit:

Guardrail Signal	AI Governance Response
DLP hard block (data classified as sensitive)	Check if high-risk AI use case is attempting to process it → escalate
DLP soft block (confidence below threshold)	Tag AI output with lower confidence; require human oversight
4-eyes review gate triggered	Automatically log as "human oversight satisfied" for that decision
Behavioral pattern anomaly	Flag as potential prohibited use pattern (e.g., social scoring)

References guardrail detections and logs, not just forward-facing use cases.

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How to Invoke

Trigger Phrases:

• "Is this AI use case compliant?"
• "Classify this AI system's risk"
• "What's our policy on [use case]?"
• "Generate a disclosure for this AI feature"
• "Define oversight requirements for this model"
• "What data can we feed into this AI system?"
• "Document the human review process for this AI decision"

Example Prompt:

Skill: ai-governance

We're building an AI system that:
- Analyzes job applicants' written responses to structured questions
- Generates a shortlist of top 10 candidates for HR review
- HR may override the shortlist but 95% of recommendations are implemented

Classify risk tier, flag policy issues, define required human oversight, 
and generate employee transparency disclosure.

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Configuration Requirements

Expects config/org-config.yaml to provide:

ai-governance:
  # Applicable regulatory frameworks
  frameworks: ["EU AI Act", "GDPR", "internal-policy"]
  
  # Risk classification scheme (you may extend)
  risk-tiers: ["Unacceptable", "High", "Limited", "Minimal"]
  
  # Who can approve high-risk use cases?
  high-risk-approval-required-by:
    - "Chief Risk Officer"
    - "Chief Compliance Officer"
    - "Legal Counsel"
  
  # Transparency & consent defaults
  transparency:
    disclosure-required-for-tiers: ["High", "Limited"]
    explicit-consent-required: true
    consent-audit-required: true
  
  # Human oversight defaults
  oversight:
    minimum-qualifications: "Domain expert or manager"
    documentation-required: true
    audit-frequency: "quarterly"
    
  # AI acceptable use policy
  ai-policies:
    prohibited-uses:
      - hiring-decisions-unreviewed
      - autonomous-financial-transfers
      - special-category-processing
      - real-time-biometric-id
    approval-required-for: ["High", "Unacceptable"]
    disclosure-required: true
    data-retention-limits: "P1Y"

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Outputs & Artifacts

• Risk Classification Report — suitable for architecture review boards
• Policy Compliance Memo — legal sign-off or exceptions
• Transparency Disclosure Templates — production-ready for UI/docs
• Human Oversight Specification — for process documentation
• Data Lineage Metadata — embedded in outputs for audit
• Risk Register Entries — auto-populates the risk-register skill

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Key Principles

1. No rubber-stamp oversight. If human oversight is required, the skill specifies what the human must actually check, not just that a button was clicked.
2. Data-driven accountability. Every AI decision is tagged with lineage so auditors can trace back.
3. Regulatory mapping, not guessing. Risk tiers map to EU AI Act Articles; obligations are cited.
4. Org policy enforcement. Your org's AI acceptable use policy is the source of truth, not generic guidelines.
5. Operational integration. Results feed guardrails, risk registers, and compliance evidence pipelines.

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See Also

• ai-governance/references/ai-risk-tiers.md — Detailed EU AI Act risk tier definitions
• risk-register skill — Auto-populates from AI governance decisions
• compliance-evidence skill — Generates control evidence for audits